Volunteer Summary

Table

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.44) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.02 (95% CI [18.48, 19.55], t(200) = 69.40, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.39, 1.10], t(200) = 0.93, p = 0.353; Std. beta = 0.17, 95% CI [-0.19, 0.53])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-0.75, 0.45], t(200) = -0.50, p = 0.617; Std. beta = -0.07, 95% CI [-0.36, 0.21])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.60, 1.24], t(200) = 0.68, p = 0.497; Std. beta = 0.15, 95% CI [-0.29, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.55) and the part related to the fixed effects alone (marginal R2) is of 7.44e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.61, 11.46], t(200) = 51.10, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.38, 0.80], t(200) = 0.69, p = 0.490; Std. beta = 0.13, 95% CI [-0.23, 0.48])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.10, 0.74], t(200) = 1.51, p = 0.132; Std. beta = 0.20, 95% CI [-0.06, 0.45])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.81, 0.50], t(200) = -0.47, p = 0.641; Std. beta = -0.09, 95% CI [-0.49, 0.30])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 7.27e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.67 (95% CI [43.72, 45.62], t(200) = 92.15, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.83, 1.81], t(200) = 0.72, p = 0.469; Std. beta = 0.13, 95% CI [-0.23, 0.49])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.21, 95% CI [-0.99, 0.58], t(200) = -0.52, p = 0.602; Std. beta = -0.06, 95% CI [-0.27, 0.16])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.95, 1.50], t(200) = 0.44, p = 0.659; Std. beta = 0.07, 95% CI [-0.26, 0.41])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 9.22e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.41 (95% CI [14.81, 16.02], t(200) = 49.66, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.79, 0.90], t(200) = 0.12, p = 0.901; Std. beta = 0.02, 95% CI [-0.34, 0.39])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.31, 0.65], t(200) = 0.69, p = 0.493; Std. beta = 0.07, 95% CI [-0.13, 0.28])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.31, 1.19], t(200) = 1.15, p = 0.252; Std. beta = 0.19, 95% CI [-0.13, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.14 (95% CI [11.77, 12.50], t(200) = 64.88, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.36, 0.66], t(200) = 0.59, p = 0.558; Std. beta = 0.11, 95% CI [-0.25, 0.46])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.63, 0.05], t(200) = -1.66, p = 0.097; Std. beta = -0.20, 95% CI [-0.44, 0.04])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.41, 95% CI [-0.12, 0.95], t(200) = 1.53, p = 0.126; Std. beta = 0.29, 95% CI [-0.08, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.34) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.02 (95% CI [10.64, 11.40], t(200) = 57.03, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-0.35, 0.70], t(200) = 0.66, p = 0.512; Std. beta = 0.12, 95% CI [-0.24, 0.48])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.36, 0.55], t(200) = 0.42, p = 0.674; Std. beta = 0.07, 95% CI [-0.24, 0.37])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.50, 0.89], t(200) = 0.55, p = 0.582; Std. beta = 0.13, 95% CI [-0.34, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.47 (95% CI [43.33, 45.60], t(200) = 76.51, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.29, 1.88], t(200) = 0.36, p = 0.717; Std. beta = 0.07, 95% CI [-0.29, 0.42])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.99, 95% CI [-2.19, 0.21], t(200) = -1.62, p = 0.105; Std. beta = -0.22, 95% CI [-0.49, 0.05])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.50, 95% CI [-1.35, 2.35], t(200) = 0.53, p = 0.596; Std. beta = 0.11, 95% CI [-0.31, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 4.85e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.67 (95% CI [24.60, 28.75], t(200) = 25.17, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-2.95, 2.83], t(200) = -0.04, p = 0.968; Std. beta = -7.55e-03, 95% CI [-0.37, 0.36])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.95, 95% CI [-0.85, 2.75], t(200) = 1.03, p = 0.301; Std. beta = 0.12, 95% CI [-0.11, 0.35])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.65, 95% CI [-4.47, 1.16], t(200) = -1.15, p = 0.250; Std. beta = -0.21, 95% CI [-0.57, 0.15])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 7.20e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.21 (95% CI [42.26, 48.16], t(200) = 30.03, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.35, 95% CI [-4.46, 3.75], t(200) = -0.17, p = 0.867; Std. beta = -0.03, 95% CI [-0.39, 0.33])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 1.98, 95% CI [-0.58, 4.53], t(200) = 1.52, p = 0.130; Std. beta = 0.17, 95% CI [-0.05, 0.40])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.05, 95% CI [-6.04, 1.94], t(200) = -1.01, p = 0.315; Std. beta = -0.18, 95% CI [-0.53, 0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 1.86e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.95 (95% CI [19.90, 22.00], t(200) = 39.05, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-1.35, 1.58], t(200) = 0.16, p = 0.876; Std. beta = 0.03, 95% CI [-0.34, 0.39])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.90, 1.05], t(200) = 0.15, p = 0.883; Std. beta = 0.02, 95% CI [-0.23, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.56, 1.49], t(200) = -0.05, p = 0.963; Std. beta = -9.08e-03, 95% CI [-0.39, 0.37])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.12 (95% CI [12.39, 13.85], t(200) = 35.21, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.67, 1.36], t(200) = 0.67, p = 0.503; Std. beta = 0.12, 95% CI [-0.24, 0.49])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.27, 95% CI [-0.96, 0.42], t(200) = -0.76, p = 0.446; Std. beta = -0.10, 95% CI [-0.34, 0.15])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.55, 95% CI [-0.53, 1.63], t(200) = 0.99, p = 0.322; Std. beta = 0.19, 95% CI [-0.19, 0.58])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 4.12e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.47 (95% CI [19.91, 21.02], t(200) = 71.86, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.56, 0.99], t(200) = 0.53, p = 0.593; Std. beta = 0.10, 95% CI [-0.26, 0.46])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 4.56e-03, 95% CI [-0.54, 0.55], t(200) = 0.02, p = 0.987; Std. beta = 2.12e-03, 95% CI [-0.25, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.71, 0.99], t(200) = 0.33, p = 0.743; Std. beta = 0.07, 95% CI [-0.33, 0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.42) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.60 (95% CI [4.34, 4.87], t(200) = 33.90, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.09, 0.65], t(200) = 1.50, p = 0.133; Std. beta = 0.27, 95% CI [-0.08, 0.63])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.16, 0.44], t(200) = 0.89, p = 0.372; Std. beta = 0.13, 95% CI [-0.16, 0.42])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.38, 0.55], t(200) = 0.37, p = 0.709; Std. beta = 0.09, 95% CI [-0.36, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.14 (95% CI [57.58, 60.69], t(200) = 74.57, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.20, 3.12], t(200) = 0.87, p = 0.385; Std. beta = 0.16, 95% CI [-0.20, 0.53])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.05, 95% CI [-1.51, 1.40], t(200) = -0.07, p = 0.945; Std. beta = -8.57e-03, 95% CI [-0.25, 0.24])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.77, 95% CI [-1.50, 3.03], t(200) = 0.66, p = 0.508; Std. beta = 0.13, 95% CI [-0.25, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.74 (95% CI [15.29, 16.19], t(200) = 68.44, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.79, 0.47], t(200) = -0.50, p = 0.615; Std. beta = -0.09, 95% CI [-0.45, 0.27])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.38, 95% CI [-0.86, 0.11], t(200) = -1.52, p = 0.128; Std. beta = -0.21, 95% CI [-0.49, 0.06])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.84, 95% CI [0.09, 1.59], t(200) = 2.19, p = 0.029; Std. beta = 0.48, 95% CI [0.05, 0.90])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.57) and the part related to the fixed effects alone (marginal R2) is of 4.54e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.53 (95% CI [12.19, 12.88], t(200) = 70.61, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.23, 95% CI [-0.71, 0.26], t(200) = -0.92, p = 0.356; Std. beta = -0.17, 95% CI [-0.53, 0.19])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.47, 0.21], t(200) = -0.74, p = 0.459; Std. beta = -0.09, 95% CI [-0.35, 0.16])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.22, 0.83], t(200) = 1.14, p = 0.252; Std. beta = 0.23, 95% CI [-0.16, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 9.35e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.28 (95% CI [27.54, 29.01], t(200) = 75.80, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-1.41, 0.63], t(200) = -0.75, p = 0.454; Std. beta = -0.14, 95% CI [-0.49, 0.22])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.48, 95% CI [-1.18, 0.22], t(200) = -1.35, p = 0.177; Std. beta = -0.17, 95% CI [-0.41, 0.08])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.12, 95% CI [0.04, 2.21], t(200) = 2.03, p = 0.043; Std. beta = 0.40, 95% CI [0.01, 0.78])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 3.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.62 (95% CI [13.47, 15.77], t(200) = 24.97, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-1.57, 1.62], t(200) = 0.03, p = 0.976; Std. beta = 5.55e-03, 95% CI [-0.36, 0.37])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.19, 95% CI [-1.11, 0.74], t(200) = -0.40, p = 0.691; Std. beta = -0.04, 95% CI [-0.25, 0.17])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.84, 95% CI [-0.61, 2.28], t(200) = 1.14, p = 0.255; Std. beta = 0.19, 95% CI [-0.14, 0.52])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.80) and the part related to the fixed effects alone (marginal R2) is of 4.69e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.66 (95% CI [2.69, 4.62], t(200) = 7.45, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-1.14, 1.54], t(200) = 0.29, p = 0.770; Std. beta = 0.05, 95% CI [-0.31, 0.42])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.52, 0.75], t(200) = 0.37, p = 0.715; Std. beta = 0.03, 95% CI [-0.14, 0.20])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.14, 0.86], t(200) = -0.28, p = 0.783; Std. beta = -0.04, 95% CI [-0.31, 0.23])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 1.44e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.38 (95% CI [2.49, 4.27], t(200) = 7.42, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.54, 0.94], t(200) = -0.47, p = 0.638; Std. beta = -0.09, 95% CI [-0.44, 0.27])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.08, 95% CI [-0.79, 0.62], t(200) = -0.23, p = 0.816; Std. beta = -0.02, 95% CI [-0.23, 0.18])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-1.02, 1.20], t(200) = 0.16, p = 0.873; Std. beta = 0.03, 95% CI [-0.29, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 3.14e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.88 (95% CI [50.10, 53.66], t(200) = 57.10, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-2.93, 2.02], t(200) = -0.36, p = 0.719; Std. beta = -0.07, 95% CI [-0.42, 0.29])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.83, 95% CI [-2.28, 0.62], t(200) = -1.12, p = 0.263; Std. beta = -0.12, 95% CI [-0.33, 0.09])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.52, 95% CI [-0.76, 3.79], t(200) = 1.31, p = 0.192; Std. beta = 0.22, 95% CI [-0.11, 0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 2.38e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.20 (95% CI [48.04, 52.37], t(200) = 45.38, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-2.94, 3.10], t(200) = 0.05, p = 0.959; Std. beta = 9.51e-03, 95% CI [-0.35, 0.37])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.99, 95% CI [-1.01, 3.00], t(200) = 0.97, p = 0.331; Std. beta = 0.12, 95% CI [-0.12, 0.36])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.51, 95% CI [-3.63, 2.61], t(200) = -0.32, p = 0.749; Std. beta = -0.06, 95% CI [-0.43, 0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Table